Overview

Dataset statistics

Number of variables32
Number of observations420000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory102.5 MiB
Average record size in memory256.0 B

Variable types

Numeric12
Categorical20

Alerts

Transaction Date and Time has a high cardinality: 418922 distinct valuesHigh cardinality
IP Address has a high cardinality: 419978 distinct valuesHigh cardinality
Transaction Date and Time is uniformly distributedUniform
IP Address is uniformly distributedUniform

Reproduction

Analysis started2023-09-17 06:49:49.665957
Analysis finished2023-09-17 06:51:32.475340
Duration1 minute and 42.81 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Transaction ID
Real number (ℝ)

Distinct418987
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55097135
Minimum10000207
Maximum99999159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:32.607264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10000207
5-th percentile14599879
Q132581946
median55105330
Q377647873
95-th percentile95536813
Maximum99999159
Range89998952
Interquartile range (IQR)45065927

Descriptive statistics

Standard deviation25983069
Coefficient of variation (CV)0.47158658
Kurtosis-1.2019034
Mean55097135
Median Absolute Deviation (MAD)22532580
Skewness-0.0010541742
Sum2.3140797 × 1013
Variance6.7511988 × 1014
MonotonicityNot monotonic
2023-09-17T06:51:32.810328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62078177 3
 
< 0.1%
84433422 3
 
< 0.1%
16396641 2
 
< 0.1%
90897318 2
 
< 0.1%
33275846 2
 
< 0.1%
43704038 2
 
< 0.1%
51892729 2
 
< 0.1%
25449421 2
 
< 0.1%
97694378 2
 
< 0.1%
92114231 2
 
< 0.1%
Other values (418977) 419978
> 99.9%
ValueCountFrequency (%)
10000207 1
< 0.1%
10000410 2
< 0.1%
10000719 1
< 0.1%
10000899 1
< 0.1%
10001295 1
< 0.1%
10001602 1
< 0.1%
10001966 1
< 0.1%
10002544 1
< 0.1%
10002588 1
< 0.1%
10002695 1
< 0.1%
ValueCountFrequency (%)
99999159 1
< 0.1%
99999102 1
< 0.1%
99998739 1
< 0.1%
99998562 1
< 0.1%
99998118 1
< 0.1%
99997961 1
< 0.1%
99997395 1
< 0.1%
99996775 1
< 0.1%
99996726 1
< 0.1%
99996677 1
< 0.1%

User ID
Real number (ℝ)

Distinct9000
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5503.342
Minimum1000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:33.019221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1451
Q13259
median5499
Q37757
95-th percentile9552
Maximum9999
Range8999
Interquartile range (IQR)4498

Descriptive statistics

Standard deviation2596.8187
Coefficient of variation (CV)0.47186215
Kurtosis-1.1987761
Mean5503.342
Median Absolute Deviation (MAD)2249
Skewness-0.00031687606
Sum2.3114036 × 109
Variance6743467.6
MonotonicityNot monotonic
2023-09-17T06:51:33.221874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7509 73
 
< 0.1%
1847 73
 
< 0.1%
8637 73
 
< 0.1%
9988 72
 
< 0.1%
8546 70
 
< 0.1%
4990 70
 
< 0.1%
6635 70
 
< 0.1%
7643 69
 
< 0.1%
8244 69
 
< 0.1%
8822 68
 
< 0.1%
Other values (8990) 419293
99.8%
ValueCountFrequency (%)
1000 42
< 0.1%
1001 42
< 0.1%
1002 43
< 0.1%
1003 37
< 0.1%
1004 38
< 0.1%
1005 47
< 0.1%
1006 48
< 0.1%
1007 38
< 0.1%
1008 49
< 0.1%
1009 35
< 0.1%
ValueCountFrequency (%)
9999 55
< 0.1%
9998 43
< 0.1%
9997 55
< 0.1%
9996 44
< 0.1%
9995 39
< 0.1%
9994 52
< 0.1%
9993 50
< 0.1%
9992 35
< 0.1%
9991 55
< 0.1%
9990 47
< 0.1%

Transaction Amount
Real number (ℝ)

Distinct98469
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.29813
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:33.419063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51.0195
Q1250.5575
median499.78
Q3750.48
95-th percentile950.1
Maximum1000
Range999
Interquartile range (IQR)499.9225

Descriptive statistics

Standard deviation288.40668
Coefficient of variation (CV)0.57646964
Kurtosis-1.2001058
Mean500.29813
Median Absolute Deviation (MAD)249.93
Skewness0.0023964758
Sum2.1012522 × 108
Variance83178.415
MonotonicityNot monotonic
2023-09-17T06:51:33.622097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
548.85 16
 
< 0.1%
144.95 15
 
< 0.1%
839.52 15
 
< 0.1%
890.38 14
 
< 0.1%
861.6 14
 
< 0.1%
823.22 14
 
< 0.1%
477.1 14
 
< 0.1%
581.9 14
 
< 0.1%
419.38 14
 
< 0.1%
28.51 14
 
< 0.1%
Other values (98459) 419856
> 99.9%
ValueCountFrequency (%)
1 3
 
< 0.1%
1.01 8
< 0.1%
1.02 4
< 0.1%
1.03 2
 
< 0.1%
1.04 2
 
< 0.1%
1.05 3
 
< 0.1%
1.06 8
< 0.1%
1.07 5
< 0.1%
1.08 1
 
< 0.1%
1.09 3
 
< 0.1%
ValueCountFrequency (%)
1000 1
 
< 0.1%
999.99 6
< 0.1%
999.98 4
< 0.1%
999.97 7
< 0.1%
999.96 5
< 0.1%
999.95 4
< 0.1%
999.94 3
< 0.1%
999.93 3
< 0.1%
999.92 4
< 0.1%
999.91 5
< 0.1%

Transaction Date and Time
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct418922
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
2021-02-14 12:34:20
 
3
2021-03-08 00:01:30
 
3
2022-09-23 11:26:44
 
3
2021-05-20 13:59:41
 
3
2023-07-05 16:47:51
 
2
Other values (418917)
419986 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters7980000
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique417848 ?
Unique (%)99.5%

Sample

1st row2022-02-11 22:27:04
2nd row2021-08-24 20:56:18
3rd row2021-07-05 05:54:22
4th row2023-07-22 15:54:35
5th row2023-05-23 08:19:00

Common Values

ValueCountFrequency (%)
2021-02-14 12:34:20 3
 
< 0.1%
2021-03-08 00:01:30 3
 
< 0.1%
2022-09-23 11:26:44 3
 
< 0.1%
2021-05-20 13:59:41 3
 
< 0.1%
2023-07-05 16:47:51 2
 
< 0.1%
2022-06-02 09:57:39 2
 
< 0.1%
2021-05-10 20:39:38 2
 
< 0.1%
2021-08-31 02:39:11 2
 
< 0.1%
2021-09-16 11:07:24 2
 
< 0.1%
2022-08-02 06:46:31 2
 
< 0.1%
Other values (418912) 419976
> 99.9%

Length

2023-09-17T06:51:33.824630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-05-28 508
 
0.1%
2021-11-06 504
 
0.1%
2021-04-27 498
 
0.1%
2021-11-08 496
 
0.1%
2023-04-23 496
 
0.1%
2022-05-15 495
 
0.1%
2022-05-13 494
 
0.1%
2021-03-24 494
 
0.1%
2021-02-28 493
 
0.1%
2021-03-03 493
 
0.1%
Other values (86654) 835029
99.4%

Most occurring characters

ValueCountFrequency (%)
2 1593795
20.0%
0 1402608
17.6%
1 951011
11.9%
- 840000
10.5%
: 840000
10.5%
3 472419
 
5.9%
420000
 
5.3%
5 342143
 
4.3%
4 341609
 
4.3%
7 201285
 
2.5%
Other values (3) 575130
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5880000
73.7%
Dash Punctuation 840000
 
10.5%
Other Punctuation 840000
 
10.5%
Space Separator 420000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1593795
27.1%
0 1402608
23.9%
1 951011
16.2%
3 472419
 
8.0%
5 342143
 
5.8%
4 341609
 
5.8%
7 201285
 
3.4%
6 200367
 
3.4%
8 188994
 
3.2%
9 185769
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 840000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 840000
100.0%
Space Separator
ValueCountFrequency (%)
420000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7980000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1593795
20.0%
0 1402608
17.6%
1 951011
11.9%
- 840000
10.5%
: 840000
10.5%
3 472419
 
5.9%
420000
 
5.3%
5 342143
 
4.3%
4 341609
 
4.3%
7 201285
 
2.5%
Other values (3) 575130
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7980000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1593795
20.0%
0 1402608
17.6%
1 951011
11.9%
- 840000
10.5%
: 840000
10.5%
3 472419
 
5.9%
420000
 
5.3%
5 342143
 
4.3%
4 341609
 
4.3%
7 201285
 
2.5%
Other values (3) 575130
 
7.2%

Merchant ID
Real number (ℝ)

Distinct9000
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5498.4712
Minimum1000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:33.994722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1453
Q13247
median5494
Q37747
95-th percentile9551
Maximum9999
Range8999
Interquartile range (IQR)4500

Descriptive statistics

Standard deviation2598.5643
Coefficient of variation (CV)0.47259761
Kurtosis-1.2005428
Mean5498.4712
Median Absolute Deviation (MAD)2250
Skewness0.0031254078
Sum2.3093579 × 109
Variance6752536.7
MonotonicityNot monotonic
2023-09-17T06:51:34.183092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7180 76
 
< 0.1%
9766 74
 
< 0.1%
5660 72
 
< 0.1%
2875 72
 
< 0.1%
9408 70
 
< 0.1%
4803 70
 
< 0.1%
9676 70
 
< 0.1%
9682 70
 
< 0.1%
2246 70
 
< 0.1%
1510 69
 
< 0.1%
Other values (8990) 419287
99.8%
ValueCountFrequency (%)
1000 48
< 0.1%
1001 49
< 0.1%
1002 57
< 0.1%
1003 51
< 0.1%
1004 47
< 0.1%
1005 46
< 0.1%
1006 55
< 0.1%
1007 38
< 0.1%
1008 44
< 0.1%
1009 41
< 0.1%
ValueCountFrequency (%)
9999 42
< 0.1%
9998 45
< 0.1%
9997 38
< 0.1%
9996 47
< 0.1%
9995 56
< 0.1%
9994 56
< 0.1%
9993 59
< 0.1%
9992 51
< 0.1%
9991 46
< 0.1%
9990 42
< 0.1%

Payment Method
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Money Order
 
10672
Cryptocurrency Wallet
 
10661
Amazon Pay
 
10656
E-check
 
10655
Neteller
 
10653
Other values (35)
366703 

Length

Max length21
Median length13
Mean length9.8061214
Min length3

Characters and Unicode

Total characters4118571
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect Debit
2nd rowPayoneer
3rd rowCheck
4th rowDiscover
5th rowPayoneer

Common Values

ValueCountFrequency (%)
Money Order 10672
 
2.5%
Cryptocurrency Wallet 10661
 
2.5%
Amazon Pay 10656
 
2.5%
E-check 10655
 
2.5%
Neteller 10653
 
2.5%
Google Wallet 10610
 
2.5%
Alipay 10609
 
2.5%
ACH Transfer 10606
 
2.5%
Mobile Wallet 10603
 
2.5%
Stripe 10601
 
2.5%
Other values (30) 313674
74.7%

Length

2023-09-17T06:51:34.397086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wallet 31874
 
5.0%
pay 31541
 
4.9%
transfer 31385
 
4.9%
card 31347
 
4.9%
debit 20932
 
3.3%
cash 20925
 
3.3%
payment 20867
 
3.3%
order 10672
 
1.7%
money 10672
 
1.7%
cryptocurrency 10661
 
1.7%
Other values (40) 419834
65.5%

Most occurring characters

ValueCountFrequency (%)
e 462697
 
11.2%
a 356377
 
8.7%
r 346540
 
8.4%
t 231175
 
5.6%
220710
 
5.4%
l 200645
 
4.9%
n 199603
 
4.8%
i 178611
 
4.3%
s 167708
 
4.1%
C 167577
 
4.1%
Other values (36) 1586928
38.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3152513
76.5%
Uppercase Letter 724377
 
17.6%
Space Separator 220710
 
5.4%
Dash Punctuation 10655
 
0.3%
Decimal Number 10316
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 462697
14.7%
a 356377
11.3%
r 346540
11.0%
t 231175
 
7.3%
l 200645
 
6.4%
n 199603
 
6.3%
i 178611
 
5.7%
s 167708
 
5.3%
o 158142
 
5.0%
y 136830
 
4.3%
Other values (14) 714185
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 167577
23.1%
P 94133
13.0%
W 73708
10.2%
A 63355
 
8.7%
D 52224
 
7.2%
M 31713
 
4.4%
S 31630
 
4.4%
E 31604
 
4.4%
B 31404
 
4.3%
T 31385
 
4.3%
Other values (9) 115644
16.0%
Space Separator
ValueCountFrequency (%)
220710
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10655
100.0%
Decimal Number
ValueCountFrequency (%)
2 10316
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3876890
94.1%
Common 241681
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 462697
 
11.9%
a 356377
 
9.2%
r 346540
 
8.9%
t 231175
 
6.0%
l 200645
 
5.2%
n 199603
 
5.1%
i 178611
 
4.6%
s 167708
 
4.3%
C 167577
 
4.3%
o 158142
 
4.1%
Other values (33) 1407815
36.3%
Common
ValueCountFrequency (%)
220710
91.3%
- 10655
 
4.4%
2 10316
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4118571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 462697
 
11.2%
a 356377
 
8.7%
r 346540
 
8.4%
t 231175
 
5.6%
220710
 
5.4%
l 200645
 
4.9%
n 199603
 
4.8%
i 178611
 
4.3%
s 167708
 
4.1%
C 167577
 
4.1%
Other values (36) 1586928
38.5%

Country Code
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
QAT
 
10721
KOR
 
10697
GER
 
10665
IND
 
10638
IDN
 
10628
Other values (35)
366651 

Length

Max length3
Median length3
Mean length2.9749071
Min length2

Characters and Unicode

Total characters1249461
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGRE
2nd rowFRA
3rd rowBRA
4th rowRUS
5th rowMEX

Common Values

ValueCountFrequency (%)
QAT 10721
 
2.6%
KOR 10697
 
2.5%
GER 10665
 
2.5%
IND 10638
 
2.5%
IDN 10628
 
2.5%
CHE 10618
 
2.5%
EGY 10604
 
2.5%
NGA 10589
 
2.5%
BRA 10585
 
2.5%
BEL 10577
 
2.5%
Other values (30) 313678
74.7%

Length

2023-09-17T06:51:34.572205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
qat 10721
 
2.6%
kor 10697
 
2.5%
ger 10665
 
2.5%
ind 10638
 
2.5%
idn 10628
 
2.5%
che 10618
 
2.5%
egy 10604
 
2.5%
nga 10589
 
2.5%
bra 10585
 
2.5%
bel 10577
 
2.5%
Other values (30) 313678
74.7%

Most occurring characters

ValueCountFrequency (%)
A 168064
13.5%
R 105422
 
8.4%
N 104825
 
8.4%
E 94438
 
7.6%
U 83928
 
6.7%
S 83485
 
6.7%
G 73830
 
5.9%
T 62932
 
5.0%
I 52457
 
4.2%
L 52320
 
4.2%
Other values (16) 367760
29.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1249461
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 168064
13.5%
R 105422
 
8.4%
N 104825
 
8.4%
E 94438
 
7.6%
U 83928
 
6.7%
S 83485
 
6.7%
G 73830
 
5.9%
T 62932
 
5.0%
I 52457
 
4.2%
L 52320
 
4.2%
Other values (16) 367760
29.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1249461
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 168064
13.5%
R 105422
 
8.4%
N 104825
 
8.4%
E 94438
 
7.6%
U 83928
 
6.7%
S 83485
 
6.7%
G 73830
 
5.9%
T 62932
 
5.0%
I 52457
 
4.2%
L 52320
 
4.2%
Other values (16) 367760
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1249461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 168064
13.5%
R 105422
 
8.4%
N 104825
 
8.4%
E 94438
 
7.6%
U 83928
 
6.7%
S 83485
 
6.7%
G 73830
 
5.9%
T 62932
 
5.0%
I 52457
 
4.2%
L 52320
 
4.2%
Other values (16) 367760
29.4%

Transaction Type
Categorical

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Refund
 
21050
Donation
 
20895
Scholarship
 
10724
Expense
 
10654
Auction
 
10631
Other values (33)
346046 

Length

Max length16
Median length13
Mean length8.0989786
Min length3

Characters and Unicode

Total characters3401571
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCashback
2nd rowFine
3rd rowRent
4th rowDonation
5th rowAcquisition

Common Values

ValueCountFrequency (%)
Refund 21050
 
5.0%
Donation 20895
 
5.0%
Scholarship 10724
 
2.6%
Expense 10654
 
2.5%
Auction 10631
 
2.5%
Gift 10604
 
2.5%
Bonus 10593
 
2.5%
Loan 10578
 
2.5%
Reimbursement 10577
 
2.5%
Transfer 10572
 
2.5%
Other values (28) 293122
69.8%

Length

2023-09-17T06:51:34.746074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
refund 21050
 
4.8%
donation 20895
 
4.7%
scholarship 10724
 
2.4%
expense 10654
 
2.4%
auction 10631
 
2.4%
gift 10604
 
2.4%
bonus 10593
 
2.4%
loan 10578
 
2.4%
reimbursement 10577
 
2.4%
transfer 10572
 
2.4%
Other values (30) 314063
71.2%

Most occurring characters

ValueCountFrequency (%)
e 367340
 
10.8%
n 315031
 
9.3%
i 283781
 
8.3%
t 283320
 
8.3%
a 230382
 
6.8%
o 210039
 
6.2%
s 179038
 
5.3%
r 168082
 
4.9%
u 115702
 
3.4%
c 104817
 
3.1%
Other values (30) 1144039
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2939689
86.4%
Uppercase Letter 440941
 
13.0%
Space Separator 20941
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 367340
12.5%
n 315031
10.7%
i 283781
9.7%
t 283320
9.6%
a 230382
 
7.8%
o 210039
 
7.1%
s 179038
 
6.1%
r 168082
 
5.7%
u 115702
 
3.9%
c 104817
 
3.6%
Other values (14) 682157
23.2%
Uppercase Letter
ValueCountFrequency (%)
R 94422
21.4%
C 52055
11.8%
S 42084
9.5%
D 41902
9.5%
A 31724
 
7.2%
P 31435
 
7.1%
I 31368
 
7.1%
T 21066
 
4.8%
F 21033
 
4.8%
B 21025
 
4.8%
Other values (5) 52827
12.0%
Space Separator
ValueCountFrequency (%)
20941
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3380630
99.4%
Common 20941
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 367340
 
10.9%
n 315031
 
9.3%
i 283781
 
8.4%
t 283320
 
8.4%
a 230382
 
6.8%
o 210039
 
6.2%
s 179038
 
5.3%
r 168082
 
5.0%
u 115702
 
3.4%
c 104817
 
3.1%
Other values (29) 1123098
33.2%
Common
ValueCountFrequency (%)
20941
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3401571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 367340
 
10.8%
n 315031
 
9.3%
i 283781
 
8.3%
t 283320
 
8.3%
a 230382
 
6.8%
o 210039
 
6.2%
s 179038
 
5.3%
r 168082
 
4.9%
u 115702
 
3.4%
c 104817
 
3.1%
Other values (30) 1144039
33.6%

Device Type
Categorical

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Smartphone
 
11197
Smart Appliance
 
11187
Smart Doorbell
 
11179
Drone
 
11172
Medical Device
 
11158
Other values (33)
364107 

Length

Max length27
Median length21
Mean length12.656757
Min length3

Characters and Unicode

Total characters5315838
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSmart Speaker
2nd rowSmart Lock
3rd rowSmart TV
4th rowVending Machine
5th rowVending Machine

Common Values

ValueCountFrequency (%)
Smartphone 11197
 
2.7%
Smart Appliance 11187
 
2.7%
Smart Doorbell 11179
 
2.7%
Drone 11172
 
2.7%
Medical Device 11158
 
2.7%
Home Security System 11150
 
2.7%
Industrial Controller 11133
 
2.7%
IoT Device 11123
 
2.6%
Embedded System 11115
 
2.6%
Vending Machine 11108
 
2.6%
Other values (28) 308478
73.4%

Length

2023-09-17T06:51:34.925690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
smart 77361
 
10.1%
device 44337
 
5.8%
system 33204
 
4.4%
home 22189
 
2.9%
reality 21999
 
2.9%
kiosk 21928
 
2.9%
smartphone 11197
 
1.5%
appliance 11187
 
1.5%
doorbell 11179
 
1.5%
drone 11172
 
1.5%
Other values (45) 496827
65.2%

Most occurring characters

ValueCountFrequency (%)
e 630028
 
11.9%
a 408746
 
7.7%
t 386291
 
7.3%
r 353868
 
6.7%
342580
 
6.4%
i 287399
 
5.4%
o 287255
 
5.4%
m 243224
 
4.6%
n 210336
 
4.0%
l 210321
 
4.0%
Other values (33) 1955790
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4078246
76.7%
Uppercase Letter 873028
 
16.4%
Space Separator 342580
 
6.4%
Dash Punctuation 21984
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 630028
15.4%
a 408746
10.0%
t 386291
9.5%
r 353868
8.7%
i 287399
 
7.0%
o 287255
 
7.0%
m 243224
 
6.0%
n 210336
 
5.2%
l 210321
 
5.2%
s 187610
 
4.6%
Other values (12) 873168
21.4%
Uppercase Letter
ValueCountFrequency (%)
S 209966
24.1%
D 88798
10.2%
T 77421
 
8.9%
M 55323
 
6.3%
C 55214
 
6.3%
R 54971
 
6.3%
A 44264
 
5.1%
H 44200
 
5.1%
V 44086
 
5.0%
I 33195
 
3.8%
Other values (9) 165590
19.0%
Space Separator
ValueCountFrequency (%)
342580
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4951274
93.1%
Common 364564
 
6.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 630028
 
12.7%
a 408746
 
8.3%
t 386291
 
7.8%
r 353868
 
7.1%
i 287399
 
5.8%
o 287255
 
5.8%
m 243224
 
4.9%
n 210336
 
4.2%
l 210321
 
4.2%
S 209966
 
4.2%
Other values (31) 1723840
34.8%
Common
ValueCountFrequency (%)
342580
94.0%
- 21984
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5315838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 630028
 
11.9%
a 408746
 
7.7%
t 386291
 
7.3%
r 353868
 
6.7%
342580
 
6.4%
i 287399
 
5.4%
o 287255
 
5.4%
m 243224
 
4.6%
n 210336
 
4.0%
l 210321
 
4.0%
Other values (33) 1955790
36.8%

IP Address
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct419978
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
36.53.163.11
 
2
243.56.224.92
 
2
106.36.177.224
 
2
218.183.138.230
 
2
86.23.35.147
 
2
Other values (419973)
419990 

Length

Max length15
Median length14
Mean length13.281226
Min length8

Characters and Unicode

Total characters5578115
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique419956 ?
Unique (%)> 99.9%

Sample

1st row157.37.179.160
2nd row117.13.255.141
3rd row153.107.127.180
4th row150.126.162.162
5th row113.171.168.73

Common Values

ValueCountFrequency (%)
36.53.163.11 2
 
< 0.1%
243.56.224.92 2
 
< 0.1%
106.36.177.224 2
 
< 0.1%
218.183.138.230 2
 
< 0.1%
86.23.35.147 2
 
< 0.1%
166.191.113.203 2
 
< 0.1%
240.109.124.195 2
 
< 0.1%
11.19.43.3 2
 
< 0.1%
228.65.193.248 2
 
< 0.1%
132.220.114.129 2
 
< 0.1%
Other values (419968) 419980
> 99.9%

Length

2023-09-17T06:51:35.138413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36.53.163.11 2
 
< 0.1%
82.235.222.236 2
 
< 0.1%
243.56.224.92 2
 
< 0.1%
109.102.208.73 2
 
< 0.1%
21.142.18.46 2
 
< 0.1%
113.194.136.139 2
 
< 0.1%
7.21.188.35 2
 
< 0.1%
187.135.194.30 2
 
< 0.1%
235.9.31.124 2
 
< 0.1%
0.198.207.78 2
 
< 0.1%
Other values (419968) 419980
> 99.9%

Most occurring characters

ValueCountFrequency (%)
. 1260000
22.6%
1 1023111
18.3%
2 735535
13.2%
4 368022
 
6.6%
3 367701
 
6.6%
5 341690
 
6.1%
0 302566
 
5.4%
9 294982
 
5.3%
7 294957
 
5.3%
6 294868
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4318115
77.4%
Other Punctuation 1260000
 
22.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1023111
23.7%
2 735535
17.0%
4 368022
 
8.5%
3 367701
 
8.5%
5 341690
 
7.9%
0 302566
 
7.0%
9 294982
 
6.8%
7 294957
 
6.8%
6 294868
 
6.8%
8 294683
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 1260000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5578115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1260000
22.6%
1 1023111
18.3%
2 735535
13.2%
4 368022
 
6.6%
3 367701
 
6.6%
5 341690
 
6.1%
0 302566
 
5.4%
9 294982
 
5.3%
7 294957
 
5.3%
6 294868
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5578115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1260000
22.6%
1 1023111
18.3%
2 735535
13.2%
4 368022
 
6.6%
3 367701
 
6.6%
5 341690
 
6.1%
0 302566
 
5.4%
9 294982
 
5.3%
7 294957
 
5.3%
6 294868
 
5.3%

Browser Type
Categorical

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Qutebrowser
 
21099
Tor Browser
 
10692
Links
 
10667
NetSurf
 
10599
Slimjet
 
10589
Other values (34)
356354 

Length

Max length17
Median length11
Mean length7.5758952
Min length3

Characters and Unicode

Total characters3181876
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrowsh
2nd rowFirefox
3rd rowOtter Browser
4th rowFalkon
5th rowTor Browser

Common Values

ValueCountFrequency (%)
Qutebrowser 21099
 
5.0%
Tor Browser 10692
 
2.5%
Links 10667
 
2.5%
NetSurf 10599
 
2.5%
Slimjet 10589
 
2.5%
Firefox 10583
 
2.5%
Midori 10580
 
2.5%
Epiphany 10578
 
2.5%
Silk 10571
 
2.5%
Internet Explorer 10566
 
2.5%
Other values (29) 303476
72.3%

Length

2023-09-17T06:51:35.354263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
browser 42235
 
8.4%
qutebrowser 21099
 
4.2%
internet 20866
 
4.1%
tor 10692
 
2.1%
links 10667
 
2.1%
netsurf 10599
 
2.1%
slimjet 10589
 
2.1%
firefox 10583
 
2.1%
midori 10580
 
2.1%
epiphany 10578
 
2.1%
Other values (33) 345426
68.5%

Most occurring characters

ValueCountFrequency (%)
r 378794
 
11.9%
e 315009
 
9.9%
o 252517
 
7.9%
a 177675
 
5.6%
n 177638
 
5.6%
i 168446
 
5.3%
t 136690
 
4.3%
s 126337
 
4.0%
S 94249
 
3.0%
w 84255
 
2.6%
Other values (36) 1270266
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2510316
78.9%
Uppercase Letter 577121
 
18.1%
Space Separator 83914
 
2.6%
Decimal Number 10525
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 378794
15.1%
e 315009
12.5%
o 252517
10.1%
a 177675
 
7.1%
n 177638
 
7.1%
i 168446
 
6.7%
t 136690
 
5.4%
s 126337
 
5.0%
w 84255
 
3.4%
u 84013
 
3.3%
Other values (14) 608942
24.3%
Uppercase Letter
ValueCountFrequency (%)
S 94249
16.3%
B 84099
14.6%
M 62905
10.9%
E 42117
 
7.3%
C 31538
 
5.5%
L 31421
 
5.4%
W 31381
 
5.4%
I 31255
 
5.4%
Q 21099
 
3.7%
O 21080
 
3.7%
Other values (10) 125977
21.8%
Space Separator
ValueCountFrequency (%)
83914
100.0%
Decimal Number
ValueCountFrequency (%)
3 10525
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3087437
97.0%
Common 94439
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 378794
 
12.3%
e 315009
 
10.2%
o 252517
 
8.2%
a 177675
 
5.8%
n 177638
 
5.8%
i 168446
 
5.5%
t 136690
 
4.4%
s 126337
 
4.1%
S 94249
 
3.1%
w 84255
 
2.7%
Other values (34) 1175827
38.1%
Common
ValueCountFrequency (%)
83914
88.9%
3 10525
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3181876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 378794
 
11.9%
e 315009
 
9.9%
o 252517
 
7.9%
a 177675
 
5.6%
n 177638
 
5.6%
i 168446
 
5.3%
t 136690
 
4.3%
s 126337
 
4.0%
S 94249
 
3.0%
w 84255
 
2.6%
Other values (36) 1270266
39.9%

Operating System
Categorical

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Android Wear
 
10668
Ubuntu
 
10659
iOS
 
10645
HarmonyOS
 
10627
Debian
 
10624
Other values (35)
366777 

Length

Max length24
Median length11
Mean length7.8728905
Min length3

Characters and Unicode

Total characters3306614
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmacOS
2nd rowopenSUSE
3rd rowWindows CE
4th rowDebian
5th rowAIX

Common Values

ValueCountFrequency (%)
Android Wear 10668
 
2.5%
Ubuntu 10659
 
2.5%
iOS 10645
 
2.5%
HarmonyOS 10627
 
2.5%
Debian 10624
 
2.5%
QNX 10612
 
2.5%
Linux 10599
 
2.5%
macOS Server 10595
 
2.5%
Firefox OS 10591
 
2.5%
Red Hat Enterprise Linux 10583
 
2.5%
Other values (30) 313797
74.7%

Length

2023-09-17T06:51:35.529261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
os 52308
 
8.9%
windows 41616
 
7.1%
android 31651
 
5.4%
linux 31590
 
5.4%
macos 21165
 
3.6%
server 20986
 
3.6%
wear 10668
 
1.8%
ubuntu 10659
 
1.8%
ios 10645
 
1.8%
harmonyos 10627
 
1.8%
Other values (33) 345918
58.8%

Most occurring characters

ValueCountFrequency (%)
i 241402
 
7.3%
e 241369
 
7.3%
n 220906
 
6.7%
S 220404
 
6.7%
r 210254
 
6.4%
o 209331
 
6.3%
a 178875
 
5.4%
167833
 
5.1%
O 136504
 
4.1%
d 125887
 
3.8%
Other values (38) 1353849
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2193876
66.3%
Uppercase Letter 934383
28.3%
Space Separator 167833
 
5.1%
Dash Punctuation 10522
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 241402
11.0%
e 241369
11.0%
n 220906
10.1%
r 210254
 
9.6%
o 209331
 
9.5%
a 178875
 
8.2%
d 125887
 
5.7%
s 83592
 
3.8%
m 73740
 
3.4%
t 73570
 
3.4%
Other values (13) 534950
24.4%
Uppercase Letter
ValueCountFrequency (%)
S 220404
23.6%
O 136504
14.6%
A 52444
 
5.6%
W 52284
 
5.6%
U 42223
 
4.5%
X 41949
 
4.5%
C 41799
 
4.5%
H 31732
 
3.4%
L 31590
 
3.4%
E 31583
 
3.4%
Other values (13) 251871
27.0%
Space Separator
ValueCountFrequency (%)
167833
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3128259
94.6%
Common 178355
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 241402
 
7.7%
e 241369
 
7.7%
n 220906
 
7.1%
S 220404
 
7.0%
r 210254
 
6.7%
o 209331
 
6.7%
a 178875
 
5.7%
O 136504
 
4.4%
d 125887
 
4.0%
s 83592
 
2.7%
Other values (36) 1259735
40.3%
Common
ValueCountFrequency (%)
167833
94.1%
- 10522
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3306614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 241402
 
7.3%
e 241369
 
7.3%
n 220906
 
6.7%
S 220404
 
6.7%
r 210254
 
6.4%
o 209331
 
6.3%
a 178875
 
5.4%
167833
 
5.1%
O 136504
 
4.1%
d 125887
 
3.8%
Other values (38) 1353849
40.9%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Industrial & Scientific
 
10687
Appliances
 
10663
Farm & Agriculture
 
10635
Pets & Animals
 
10631
Financial Services
 
10621
Other values (35)
366763 

Length

Max length23
Median length17
Mean length14.851748
Min length7

Characters and Unicode

Total characters6237734
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFitness & Nutrition
2nd rowJewelry
3rd rowFurniture
4th rowElectronics Repair
5th rowWedding & Bridal

Common Values

ValueCountFrequency (%)
Industrial & Scientific 10687
 
2.5%
Appliances 10663
 
2.5%
Farm & Agriculture 10635
 
2.5%
Pets & Animals 10631
 
2.5%
Financial Services 10621
 
2.5%
Wedding & Bridal 10611
 
2.5%
Home & Garden 10609
 
2.5%
Sports & Outdoors 10584
 
2.5%
Baby & Maternity 10583
 
2.5%
Charity & Nonprofit 10576
 
2.5%
Other values (30) 313800
74.7%

Length

2023-09-17T06:51:35.715456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
210725
 
22.3%
services 31370
 
3.3%
home 21137
 
2.2%
electronics 20913
 
2.2%
industrial 10687
 
1.1%
scientific 10687
 
1.1%
appliances 10663
 
1.1%
agriculture 10635
 
1.1%
farm 10635
 
1.1%
animals 10631
 
1.1%
Other values (57) 597702
63.2%

Most occurring characters

ValueCountFrequency (%)
e 587030
 
9.4%
525785
 
8.4%
i 462448
 
7.4%
t 451301
 
7.2%
r 388786
 
6.2%
s 357090
 
5.7%
n 347317
 
5.6%
o 335493
 
5.4%
a 304860
 
4.9%
l 262008
 
4.2%
Other values (35) 2215616
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4766164
76.4%
Uppercase Letter 735060
 
11.8%
Space Separator 525785
 
8.4%
Other Punctuation 210725
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 587030
12.3%
i 462448
9.7%
t 451301
9.5%
r 388786
 
8.2%
s 357090
 
7.5%
n 347317
 
7.3%
o 335493
 
7.0%
a 304860
 
6.4%
l 262008
 
5.5%
c 220093
 
4.6%
Other values (14) 1049738
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 104696
14.2%
A 63235
 
8.6%
B 62819
 
8.5%
F 52857
 
7.2%
E 52496
 
7.1%
C 52149
 
7.1%
H 42195
 
5.7%
G 42086
 
5.7%
T 42062
 
5.7%
I 31721
 
4.3%
Other values (9) 188744
25.7%
Space Separator
ValueCountFrequency (%)
525785
100.0%
Other Punctuation
ValueCountFrequency (%)
& 210725
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5501224
88.2%
Common 736510
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 587030
 
10.7%
i 462448
 
8.4%
t 451301
 
8.2%
r 388786
 
7.1%
s 357090
 
6.5%
n 347317
 
6.3%
o 335493
 
6.1%
a 304860
 
5.5%
l 262008
 
4.8%
c 220093
 
4.0%
Other values (33) 1784798
32.4%
Common
ValueCountFrequency (%)
525785
71.4%
& 210725
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6237734
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 587030
 
9.4%
525785
 
8.4%
i 462448
 
7.4%
t 451301
 
7.2%
r 388786
 
6.2%
s 357090
 
5.7%
n 347317
 
5.6%
o 335493
 
5.4%
a 304860
 
4.9%
l 262008
 
4.2%
Other values (35) 2215616
35.5%

User Age
Real number (ℝ)

Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.011221
Minimum18
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:36.318075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q133
median49
Q365
95-th percentile77
Maximum80
Range62
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.186066
Coefficient of variation (CV)0.37105922
Kurtosis-1.2020499
Mean49.011221
Median Absolute Deviation (MAD)16
Skewness-0.0018995806
Sum20584713
Variance330.73298
MonotonicityNot monotonic
2023-09-17T06:51:36.513987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59 6840
 
1.6%
72 6834
 
1.6%
70 6820
 
1.6%
54 6796
 
1.6%
27 6787
 
1.6%
51 6772
 
1.6%
46 6771
 
1.6%
21 6754
 
1.6%
74 6749
 
1.6%
42 6744
 
1.6%
Other values (53) 352133
83.8%
ValueCountFrequency (%)
18 6707
1.6%
19 6599
1.6%
20 6624
1.6%
21 6754
1.6%
22 6671
1.6%
23 6653
1.6%
24 6684
1.6%
25 6624
1.6%
26 6648
1.6%
27 6787
1.6%
ValueCountFrequency (%)
80 6631
1.6%
79 6704
1.6%
78 6472
1.5%
77 6619
1.6%
76 6657
1.6%
75 6728
1.6%
74 6749
1.6%
73 6648
1.6%
72 6834
1.6%
71 6715
1.6%

User Occupation
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Firefighter
 
16497
Engineer
 
16359
Physicist
 
16276
Geologist
 
16258
Software Developer
 
16243
Other values (21)
338367 

Length

Max length18
Median length13
Mean length9.2724881
Min length4

Characters and Unicode

Total characters3894445
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPolice Officer
2nd rowDoctor
3rd rowElectrician
4th rowChef
5th rowEngineer

Common Values

ValueCountFrequency (%)
Firefighter 16497
 
3.9%
Engineer 16359
 
3.9%
Physicist 16276
 
3.9%
Geologist 16258
 
3.9%
Software Developer 16243
 
3.9%
Teacher 16227
 
3.9%
Event Planner 16208
 
3.9%
Chef 16203
 
3.9%
Plumber 16180
 
3.9%
Salesperson 16180
 
3.9%
Other values (16) 257369
61.3%

Length

2023-09-17T06:51:36.711858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
firefighter 16497
 
3.4%
engineer 16359
 
3.4%
physicist 16276
 
3.4%
geologist 16258
 
3.4%
software 16243
 
3.4%
developer 16243
 
3.4%
teacher 16227
 
3.3%
event 16208
 
3.3%
planner 16208
 
3.3%
chef 16203
 
3.3%
Other values (20) 321828
66.4%

Most occurring characters

ValueCountFrequency (%)
e 517656
13.3%
r 355718
 
9.1%
i 323604
 
8.3%
t 290837
 
7.5%
o 258145
 
6.6%
n 210133
 
5.4%
s 209925
 
5.4%
c 209375
 
5.4%
a 193742
 
5.0%
l 161403
 
4.1%
Other values (26) 1163907
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3345345
85.9%
Uppercase Letter 484550
 
12.4%
Space Separator 64550
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 517656
15.5%
r 355718
10.6%
i 323604
9.7%
t 290837
8.7%
o 258145
7.7%
n 210133
 
6.3%
s 209925
 
6.3%
c 209375
 
6.3%
a 193742
 
5.8%
l 161403
 
4.8%
Other values (10) 614807
18.4%
Uppercase Letter
ValueCountFrequency (%)
P 80657
16.6%
E 48718
10.1%
S 48558
10.0%
D 48483
10.0%
C 48341
10.0%
A 48223
10.0%
F 32580
6.7%
G 16258
 
3.4%
T 16227
 
3.3%
B 16175
 
3.3%
Other values (5) 80330
16.6%
Space Separator
ValueCountFrequency (%)
64550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3829895
98.3%
Common 64550
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 517656
13.5%
r 355718
 
9.3%
i 323604
 
8.4%
t 290837
 
7.6%
o 258145
 
6.7%
n 210133
 
5.5%
s 209925
 
5.5%
c 209375
 
5.5%
a 193742
 
5.1%
l 161403
 
4.2%
Other values (25) 1099357
28.7%
Common
ValueCountFrequency (%)
64550
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3894445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 517656
13.3%
r 355718
 
9.1%
i 323604
 
8.3%
t 290837
 
7.5%
o 258145
 
6.6%
n 210133
 
5.4%
s 209925
 
5.4%
c 209375
 
5.4%
a 193742
 
5.0%
l 161403
 
4.1%
Other values (26) 1163907
29.9%

User Income
Real number (ℝ)

Distinct411300
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50460.594
Minimum1000.25
Maximum99999.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:36.901091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000.25
5-th percentile5870.8625
Q125653.862
median50498.245
Q375217.688
95-th percentile95009.133
Maximum99999.96
Range98999.71
Interquartile range (IQR)49563.825

Descriptive statistics

Standard deviation28609.041
Coefficient of variation (CV)0.56695806
Kurtosis-1.201963
Mean50460.594
Median Absolute Deviation (MAD)24780.995
Skewness-0.0008193249
Sum2.1193449 × 1010
Variance8.1847721 × 108
MonotonicityNot monotonic
2023-09-17T06:51:37.095352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10307.72 3
 
< 0.1%
80491.25 3
 
< 0.1%
12416.25 3
 
< 0.1%
17064.27 3
 
< 0.1%
64068.08 3
 
< 0.1%
39960.96 3
 
< 0.1%
86583.02 3
 
< 0.1%
42333.98 3
 
< 0.1%
98836.21 3
 
< 0.1%
75415.17 3
 
< 0.1%
Other values (411290) 419970
> 99.9%
ValueCountFrequency (%)
1000.25 1
< 0.1%
1000.62 1
< 0.1%
1001.1 1
< 0.1%
1001.66 1
< 0.1%
1001.69 1
< 0.1%
1001.74 1
< 0.1%
1002.11 1
< 0.1%
1002.15 1
< 0.1%
1002.79 2
< 0.1%
1002.86 1
< 0.1%
ValueCountFrequency (%)
99999.96 1
< 0.1%
99999.74 1
< 0.1%
99999.62 1
< 0.1%
99999.61 1
< 0.1%
99999.46 1
< 0.1%
99999.35 1
< 0.1%
99998.71 1
< 0.1%
99998.65 1
< 0.1%
99998.64 1
< 0.1%
99998.56 1
< 0.1%

User Gender
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Genderqueer
60259 
Male
60187 
Non-Binary
60112 
Agender
60101 
Other
59918 
Other values (2)
119423 

Length

Max length17
Median length7
Mean length8.5703833
Min length4

Characters and Unicode

Total characters3599561
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowNon-Binary
3rd rowFemale
4th rowFemale
5th rowMale

Common Values

ValueCountFrequency (%)
Genderqueer 60259
14.3%
Male 60187
14.3%
Non-Binary 60112
14.3%
Agender 60101
14.3%
Other 59918
14.3%
Prefer Not to Say 59819
14.2%
Female 59604
14.2%

Length

2023-09-17T06:51:37.388841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-17T06:51:37.766328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
genderqueer 60259
10.1%
male 60187
10.0%
non-binary 60112
10.0%
agender 60101
10.0%
other 59918
10.0%
prefer 59819
10.0%
not 59819
10.0%
to 59819
10.0%
say 59819
10.0%
female 59604
9.9%

Most occurring characters

ValueCountFrequency (%)
e 720189
20.0%
r 420287
 
11.7%
n 240584
 
6.7%
a 239722
 
6.7%
o 179750
 
5.0%
t 179556
 
5.0%
179457
 
5.0%
d 120360
 
3.3%
y 119931
 
3.3%
N 119931
 
3.3%
Other values (17) 1079794
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2760242
76.7%
Uppercase Letter 599750
 
16.7%
Space Separator 179457
 
5.0%
Dash Punctuation 60112
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 720189
26.1%
r 420287
15.2%
n 240584
 
8.7%
a 239722
 
8.7%
o 179750
 
6.5%
t 179556
 
6.5%
d 120360
 
4.4%
y 119931
 
4.3%
l 119791
 
4.3%
q 60259
 
2.2%
Other values (6) 359813
13.0%
Uppercase Letter
ValueCountFrequency (%)
N 119931
20.0%
G 60259
10.0%
M 60187
10.0%
B 60112
10.0%
A 60101
10.0%
O 59918
10.0%
P 59819
10.0%
S 59819
10.0%
F 59604
9.9%
Space Separator
ValueCountFrequency (%)
179457
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3359992
93.3%
Common 239569
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 720189
21.4%
r 420287
12.5%
n 240584
 
7.2%
a 239722
 
7.1%
o 179750
 
5.3%
t 179556
 
5.3%
d 120360
 
3.6%
y 119931
 
3.6%
N 119931
 
3.6%
l 119791
 
3.6%
Other values (15) 899891
26.8%
Common
ValueCountFrequency (%)
179457
74.9%
- 60112
 
25.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3599561
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 720189
20.0%
r 420287
 
11.7%
n 240584
 
6.7%
a 239722
 
6.7%
o 179750
 
5.0%
t 179556
 
5.0%
179457
 
5.0%
d 120360
 
3.3%
y 119931
 
3.3%
N 119931
 
3.3%
Other values (17) 1079794
30.0%
Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Trial
 
23633
VIP
 
23609
Inactive
 
23473
Premium
 
23441
Suspended
 
23438
Other values (13)
302406 

Length

Max length16
Median length9
Mean length6.7756619
Min length3

Characters and Unicode

Total characters2845778
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowFree
3rd rowMember
4th rowMember
5th rowInactive

Common Values

ValueCountFrequency (%)
Trial 23633
 
5.6%
VIP 23609
 
5.6%
Inactive 23473
 
5.6%
Premium 23441
 
5.6%
Suspended 23438
 
5.6%
Limited 23427
 
5.6%
Standard 23414
 
5.6%
Member 23367
 
5.6%
Pending Approval 23346
 
5.6%
Pro 23345
 
5.6%
Other values (8) 185507
44.2%

Length

2023-09-17T06:51:38.163020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
trial 23633
 
5.3%
vip 23609
 
5.3%
inactive 23473
 
5.3%
premium 23441
 
5.3%
suspended 23438
 
5.3%
limited 23427
 
5.3%
standard 23414
 
5.3%
member 23367
 
5.3%
approval 23346
 
5.3%
pending 23346
 
5.3%
Other values (9) 208852
47.1%

Most occurring characters

ValueCountFrequency (%)
e 419071
14.7%
i 302493
 
10.6%
r 209968
 
7.4%
d 209906
 
7.4%
n 163454
 
5.7%
t 139968
 
4.9%
a 117280
 
4.1%
P 93741
 
3.3%
m 93676
 
3.3%
v 93329
 
3.3%
Other values (25) 1002892
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2331868
81.9%
Uppercase Letter 490564
 
17.2%
Space Separator 23346
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 419071
18.0%
i 302493
13.0%
r 209968
9.0%
d 209906
9.0%
n 163454
 
7.0%
t 139968
 
6.0%
a 117280
 
5.0%
m 93676
 
4.0%
v 93329
 
4.0%
s 93189
 
4.0%
Other values (10) 489534
21.0%
Uppercase Letter
ValueCountFrequency (%)
P 93741
19.1%
I 47082
9.6%
S 46852
9.6%
A 46574
9.5%
V 46431
9.5%
T 23633
 
4.8%
L 23427
 
4.8%
M 23367
 
4.8%
C 23325
 
4.8%
F 23318
 
4.8%
Other values (4) 92814
18.9%
Space Separator
ValueCountFrequency (%)
23346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2822432
99.2%
Common 23346
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 419071
14.8%
i 302493
 
10.7%
r 209968
 
7.4%
d 209906
 
7.4%
n 163454
 
5.8%
t 139968
 
5.0%
a 117280
 
4.2%
P 93741
 
3.3%
m 93676
 
3.3%
v 93329
 
3.3%
Other values (24) 979546
34.7%
Common
ValueCountFrequency (%)
23346
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2845778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 419071
14.7%
i 302493
 
10.6%
r 209968
 
7.4%
d 209906
 
7.4%
n 163454
 
5.7%
t 139968
 
4.9%
a 117280
 
4.1%
P 93741
 
3.3%
m 93676
 
3.3%
v 93329
 
3.3%
Other values (25) 1002892
35.2%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Sent
 
10729
Pending Payment
 
10625
Failed
 
10615
Refunded
 
10614
Blocked
 
10603
Other values (35)
366814 

Length

Max length21
Median length17
Mean length9.5273405
Min length4

Characters and Unicode

Total characters4001483
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowError
2nd rowDebit
3rd rowIn Progress
4th rowVoid
5th rowUnder Review

Common Values

ValueCountFrequency (%)
Sent 10729
 
2.6%
Pending Payment 10625
 
2.5%
Failed 10615
 
2.5%
Refunded 10614
 
2.5%
Blocked 10603
 
2.5%
Declined 10600
 
2.5%
Processing 10576
 
2.5%
Cancelled 10565
 
2.5%
Pending Review 10565
 
2.5%
Closed 10563
 
2.5%
Other values (30) 313945
74.7%

Length

2023-09-17T06:51:38.540055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pending 42091
 
7.9%
review 21123
 
3.9%
declined 21112
 
3.9%
in 21059
 
3.9%
partially 20930
 
3.9%
approved 20848
 
3.9%
sent 10729
 
2.0%
payment 10625
 
2.0%
failed 10615
 
2.0%
refunded 10614
 
2.0%
Other values (33) 345987
64.6%

Most occurring characters

ValueCountFrequency (%)
e 650925
16.3%
d 367417
 
9.2%
i 336128
 
8.4%
r 272744
 
6.8%
n 263642
 
6.6%
t 199115
 
5.0%
l 189112
 
4.7%
o 188761
 
4.7%
a 136858
 
3.4%
s 126008
 
3.1%
Other values (30) 1270773
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3360491
84.0%
Uppercase Letter 525259
 
13.1%
Space Separator 115733
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 650925
19.4%
d 367417
10.9%
i 336128
10.0%
r 272744
8.1%
n 263642
7.8%
t 199115
 
5.9%
l 189112
 
5.6%
o 188761
 
5.6%
a 136858
 
4.1%
s 126008
 
3.7%
Other values (15) 629781
18.7%
Uppercase Letter
ValueCountFrequency (%)
P 115588
22.0%
C 63195
12.0%
R 63004
12.0%
D 52395
10.0%
A 41814
 
8.0%
S 31542
 
6.0%
V 31457
 
6.0%
T 21075
 
4.0%
I 21059
 
4.0%
U 21009
 
4.0%
Other values (4) 63121
12.0%
Space Separator
ValueCountFrequency (%)
115733
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3885750
97.1%
Common 115733
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 650925
16.8%
d 367417
 
9.5%
i 336128
 
8.7%
r 272744
 
7.0%
n 263642
 
6.8%
t 199115
 
5.1%
l 189112
 
4.9%
o 188761
 
4.9%
a 136858
 
3.5%
s 126008
 
3.2%
Other values (29) 1155040
29.7%
Common
ValueCountFrequency (%)
115733
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4001483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 650925
16.3%
d 367417
 
9.2%
i 336128
 
8.4%
r 272744
 
6.8%
n 263642
 
6.6%
t 199115
 
5.0%
l 189112
 
4.7%
o 188761
 
4.7%
a 136858
 
3.4%
s 126008
 
3.1%
Other values (30) 1270773
31.8%

Location Distance
Real number (ℝ)

Distinct9901
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.504677
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:38.900380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.9895
Q125.72
median50.5
Q375.23
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.51

Descriptive statistics

Standard deviation28.571549
Coefficient of variation (CV)0.56572084
Kurtosis-1.2015989
Mean50.504677
Median Absolute Deviation (MAD)24.76
Skewness0.00040456792
Sum21211965
Variance816.33339
MonotonicityNot monotonic
2023-09-17T06:51:39.264558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 70
 
< 0.1%
17.62 67
 
< 0.1%
52.39 67
 
< 0.1%
17.07 66
 
< 0.1%
91.18 66
 
< 0.1%
58.88 66
 
< 0.1%
99.44 66
 
< 0.1%
40.89 65
 
< 0.1%
4.39 65
 
< 0.1%
2.69 65
 
< 0.1%
Other values (9891) 419337
99.8%
ValueCountFrequency (%)
1 13
 
< 0.1%
1.01 40
< 0.1%
1.02 35
< 0.1%
1.03 38
< 0.1%
1.04 30
< 0.1%
1.05 36
< 0.1%
1.06 43
< 0.1%
1.07 42
< 0.1%
1.08 44
< 0.1%
1.09 42
< 0.1%
ValueCountFrequency (%)
100 26
< 0.1%
99.99 42
< 0.1%
99.98 48
< 0.1%
99.97 27
< 0.1%
99.96 34
< 0.1%
99.95 47
< 0.1%
99.94 36
< 0.1%
99.93 44
< 0.1%
99.92 36
< 0.1%
99.91 40
< 0.1%
Distinct5901
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.555903
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:39.633886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.96
Q115.8
median30.6
Q345.33
95-th percentile57.06
Maximum60
Range59
Interquartile range (IQR)29.53

Descriptive statistics

Standard deviation17.049644
Coefficient of variation (CV)0.55798198
Kurtosis-1.202061
Mean30.555903
Median Absolute Deviation (MAD)14.77
Skewness-0.0057498815
Sum12833479
Variance290.69034
MonotonicityNot monotonic
2023-09-17T06:51:40.024821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.95 106
 
< 0.1%
37.12 105
 
< 0.1%
23.63 105
 
< 0.1%
53.56 104
 
< 0.1%
2.94 102
 
< 0.1%
49.41 101
 
< 0.1%
36.65 100
 
< 0.1%
30.5 99
 
< 0.1%
24.9 99
 
< 0.1%
6.45 98
 
< 0.1%
Other values (5891) 418981
99.8%
ValueCountFrequency (%)
1 38
< 0.1%
1.01 56
< 0.1%
1.02 85
< 0.1%
1.03 88
< 0.1%
1.04 63
< 0.1%
1.05 84
< 0.1%
1.06 67
< 0.1%
1.07 70
< 0.1%
1.08 73
< 0.1%
1.09 75
< 0.1%
ValueCountFrequency (%)
60 30
 
< 0.1%
59.99 75
< 0.1%
59.98 70
< 0.1%
59.97 60
< 0.1%
59.96 55
< 0.1%
59.95 72
< 0.1%
59.94 82
< 0.1%
59.93 76
< 0.1%
59.92 70
< 0.1%
59.91 72
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Nighttime
140247 
Evening
139903 
Daytime
139850 

Length

Max length9
Median length7
Mean length7.6678429
Min length7

Characters and Unicode

Total characters3220494
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvening
2nd rowNighttime
3rd rowDaytime
4th rowDaytime
5th rowEvening

Common Values

ValueCountFrequency (%)
Nighttime 140247
33.4%
Evening 139903
33.3%
Daytime 139850
33.3%

Length

2023-09-17T06:51:40.492131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-17T06:51:40.831991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nighttime 140247
33.4%
evening 139903
33.3%
daytime 139850
33.3%

Most occurring characters

ValueCountFrequency (%)
i 560247
17.4%
t 420344
13.1%
e 420000
13.0%
g 280150
8.7%
m 280097
8.7%
n 279806
8.7%
N 140247
 
4.4%
h 140247
 
4.4%
E 139903
 
4.3%
v 139903
 
4.3%
Other values (3) 419550
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2800494
87.0%
Uppercase Letter 420000
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 560247
20.0%
t 420344
15.0%
e 420000
15.0%
g 280150
10.0%
m 280097
10.0%
n 279806
10.0%
h 140247
 
5.0%
v 139903
 
5.0%
a 139850
 
5.0%
y 139850
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
N 140247
33.4%
E 139903
33.3%
D 139850
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 3220494
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 560247
17.4%
t 420344
13.1%
e 420000
13.0%
g 280150
8.7%
m 280097
8.7%
n 279806
8.7%
N 140247
 
4.4%
h 140247
 
4.4%
E 139903
 
4.3%
v 139903
 
4.3%
Other values (3) 419550
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3220494
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 560247
17.4%
t 420344
13.1%
e 420000
13.0%
g 280150
8.7%
m 280097
8.7%
n 279806
8.7%
N 140247
 
4.4%
h 140247
 
4.4%
E 139903
 
4.3%
v 139903
 
4.3%
Other values (3) 419550
13.0%
Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.534569
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:41.010488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median51
Q376
95-th percentile95
Maximum100
Range99
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.850937
Coefficient of variation (CV)0.57091487
Kurtosis-1.1994281
Mean50.534569
Median Absolute Deviation (MAD)25
Skewness-0.0032846513
Sum21224519
Variance832.37656
MonotonicityNot monotonic
2023-09-17T06:51:41.213357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 4344
 
1.0%
40 4339
 
1.0%
61 4337
 
1.0%
18 4323
 
1.0%
3 4322
 
1.0%
19 4320
 
1.0%
95 4317
 
1.0%
12 4309
 
1.0%
28 4292
 
1.0%
58 4291
 
1.0%
Other values (90) 376806
89.7%
ValueCountFrequency (%)
1 4240
1.0%
2 4187
1.0%
3 4322
1.0%
4 4220
1.0%
5 4080
1.0%
6 4233
1.0%
7 4176
1.0%
8 4182
1.0%
9 4162
1.0%
10 4143
1.0%
ValueCountFrequency (%)
100 4109
1.0%
99 4173
1.0%
98 4109
1.0%
97 4166
1.0%
96 4254
1.0%
95 4317
1.0%
94 4125
1.0%
93 4191
1.0%
92 4245
1.0%
91 4210
1.0%
Distinct401
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9961925
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:41.427663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q11.99
median2.99
Q34
95-th percentile4.8
Maximum5
Range4
Interquartile range (IQR)2.01

Descriptive statistics

Standard deviation1.1557042
Coefficient of variation (CV)0.38572429
Kurtosis-1.2012354
Mean2.9961925
Median Absolute Deviation (MAD)1
Skewness0.0063485419
Sum1258400.9
Variance1.3356523
MonotonicityNot monotonic
2023-09-17T06:51:41.640061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.93 1162
 
0.3%
2.05 1147
 
0.3%
3.01 1136
 
0.3%
1.25 1127
 
0.3%
1.67 1124
 
0.3%
4.57 1118
 
0.3%
1.4 1117
 
0.3%
1.28 1115
 
0.3%
2.55 1113
 
0.3%
3.31 1113
 
0.3%
Other values (391) 408728
97.3%
ValueCountFrequency (%)
1 529
0.1%
1.01 1041
0.2%
1.02 1096
0.3%
1.03 1040
0.2%
1.04 1090
0.3%
1.05 1002
0.2%
1.06 1058
0.3%
1.07 1034
0.2%
1.08 1020
0.2%
1.09 1054
0.3%
ValueCountFrequency (%)
5 559
0.1%
4.99 1037
0.2%
4.98 1082
0.3%
4.97 1051
0.3%
4.96 1015
0.2%
4.95 1058
0.3%
4.94 1036
0.2%
4.93 1162
0.3%
4.92 1038
0.2%
4.91 1063
0.3%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Qatar
 
10665
Turkey
 
10646
Thailand
 
10624
Poland
 
10617
New Zealand
 
10613
Other values (35)
366835 

Length

Max length20
Median length13
Mean length7.93875
Min length5

Characters and Unicode

Total characters3334275
Distinct characters44
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpain
2nd rowSpain
3rd rowQatar
4th rowUnited Kingdom
5th rowChina

Common Values

ValueCountFrequency (%)
Qatar 10665
 
2.5%
Turkey 10646
 
2.5%
Thailand 10624
 
2.5%
Poland 10617
 
2.5%
New Zealand 10613
 
2.5%
Germany 10612
 
2.5%
Greece 10598
 
2.5%
Norway 10587
 
2.5%
Singapore 10584
 
2.5%
Belgium 10582
 
2.5%
Other values (30) 313872
74.7%

Length

2023-09-17T06:51:41.839978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united 31163
 
6.1%
south 20748
 
4.0%
qatar 10665
 
2.1%
turkey 10646
 
2.1%
thailand 10624
 
2.1%
poland 10617
 
2.1%
new 10613
 
2.1%
zealand 10613
 
2.1%
germany 10612
 
2.1%
greece 10598
 
2.1%
Other values (36) 376943
73.4%

Most occurring characters

ValueCountFrequency (%)
a 472026
14.2%
e 283541
 
8.5%
i 282406
 
8.5%
n 272665
 
8.2%
r 209757
 
6.3%
t 177622
 
5.3%
d 146713
 
4.4%
l 115990
 
3.5%
o 115488
 
3.5%
s 104610
 
3.1%
Other values (34) 1153457
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2726591
81.8%
Uppercase Letter 513842
 
15.4%
Space Separator 93842
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 472026
17.3%
e 283541
10.4%
i 282406
10.4%
n 272665
10.0%
r 209757
 
7.7%
t 177622
 
6.5%
d 146713
 
5.4%
l 115990
 
4.3%
o 115488
 
4.2%
s 104610
 
3.8%
Other values (13) 545773
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 83292
16.2%
A 62538
12.2%
I 42128
 
8.2%
N 42060
 
8.2%
T 31736
 
6.2%
K 31317
 
6.1%
U 31163
 
6.1%
G 21210
 
4.1%
B 21151
 
4.1%
M 21139
 
4.1%
Other values (10) 126108
24.5%
Space Separator
ValueCountFrequency (%)
93842
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3240433
97.2%
Common 93842
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 472026
14.6%
e 283541
 
8.8%
i 282406
 
8.7%
n 272665
 
8.4%
r 209757
 
6.5%
t 177622
 
5.5%
d 146713
 
4.5%
l 115990
 
3.6%
o 115488
 
3.6%
s 104610
 
3.2%
Other values (33) 1059615
32.7%
Common
ValueCountFrequency (%)
93842
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3334275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 472026
14.2%
e 283541
 
8.5%
i 282406
 
8.5%
n 272665
 
8.2%
r 209757
 
6.3%
t 177622
 
5.3%
d 146713
 
4.4%
l 115990
 
3.5%
o 115488
 
3.5%
s 104610
 
3.1%
Other values (34) 1153457
34.6%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
ARS
 
10711
THB
 
10668
CZK
 
10636
VND
 
10600
RUB
 
10597
Other values (35)
366788 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1260000
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNOK
2nd rowRON
3rd rowIDN
4th rowARS
5th rowSAR

Common Values

ValueCountFrequency (%)
ARS 10711
 
2.6%
THB 10668
 
2.5%
CZK 10636
 
2.5%
VND 10600
 
2.5%
RUB 10597
 
2.5%
TRY 10597
 
2.5%
SAR 10596
 
2.5%
CNY 10590
 
2.5%
HKD 10581
 
2.5%
MXN 10580
 
2.5%
Other values (30) 313844
74.7%

Length

2023-09-17T06:51:41.999855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ars 10711
 
2.6%
thb 10668
 
2.5%
czk 10636
 
2.5%
vnd 10600
 
2.5%
rub 10597
 
2.5%
try 10597
 
2.5%
sar 10596
 
2.5%
cny 10590
 
2.5%
hkd 10581
 
2.5%
mxn 10580
 
2.5%
Other values (30) 313844
74.7%

Most occurring characters

ValueCountFrequency (%)
R 126254
 
10.0%
N 104910
 
8.3%
D 104733
 
8.3%
P 94535
 
7.5%
K 83752
 
6.6%
A 73750
 
5.9%
S 73487
 
5.8%
C 62982
 
5.0%
E 62616
 
5.0%
U 52709
 
4.2%
Other values (16) 420272
33.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1260000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 126254
 
10.0%
N 104910
 
8.3%
D 104733
 
8.3%
P 94535
 
7.5%
K 83752
 
6.6%
A 73750
 
5.9%
S 73487
 
5.8%
C 62982
 
5.0%
E 62616
 
5.0%
U 52709
 
4.2%
Other values (16) 420272
33.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 1260000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 126254
 
10.0%
N 104910
 
8.3%
D 104733
 
8.3%
P 94535
 
7.5%
K 83752
 
6.6%
A 73750
 
5.9%
S 73487
 
5.8%
C 62982
 
5.0%
E 62616
 
5.0%
U 52709
 
4.2%
Other values (16) 420272
33.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1260000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 126254
 
10.0%
N 104910
 
8.3%
D 104733
 
8.3%
P 94535
 
7.5%
K 83752
 
6.6%
A 73750
 
5.9%
S 73487
 
5.8%
C 62982
 
5.0%
E 62616
 
5.0%
U 52709
 
4.2%
Other values (16) 420272
33.4%
Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Insurance Premium
 
11314
Donation to Nonprofit
 
11212
Retail Purchase
 
11209
Settlement
 
11184
Buyback
 
11167
Other values (33)
363914 

Length

Max length21
Median length16
Mean length13.008264
Min length4

Characters and Unicode

Total characters5463471
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyalty
2nd rowRental Payment
3rd rowRent
4th rowInsurance Premium
5th rowProduct Purchase

Common Values

ValueCountFrequency (%)
Insurance Premium 11314
 
2.7%
Donation to Nonprofit 11212
 
2.7%
Retail Purchase 11209
 
2.7%
Settlement 11184
 
2.7%
Buyback 11167
 
2.7%
Gift Purchase 11162
 
2.7%
Consultation Fee 11161
 
2.7%
Bill Payment 11141
 
2.7%
Auction Bid 11140
 
2.7%
Compensation 11137
 
2.7%
Other values (28) 308173
73.4%

Length

2023-09-17T06:51:42.169959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
payment 66120
 
9.3%
purchase 44562
 
6.3%
to 22343
 
3.2%
donation 22291
 
3.1%
settlement 22264
 
3.1%
fee 22104
 
3.1%
insurance 11314
 
1.6%
premium 11314
 
1.6%
nonprofit 11212
 
1.6%
retail 11209
 
1.6%
Other values (42) 463222
65.4%

Most occurring characters

ValueCountFrequency (%)
e 629314
 
11.5%
t 497561
 
9.1%
n 453181
 
8.3%
i 386463
 
7.1%
a 376416
 
6.9%
287955
 
5.3%
o 277224
 
5.1%
s 254010
 
4.6%
r 233000
 
4.3%
m 209966
 
3.8%
Other values (31) 1858381
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4489904
82.2%
Uppercase Letter 685612
 
12.5%
Space Separator 287955
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 629314
14.0%
t 497561
11.1%
n 453181
10.1%
i 386463
 
8.6%
a 376416
 
8.4%
o 277224
 
6.2%
s 254010
 
5.7%
r 233000
 
5.2%
m 209966
 
4.7%
c 177450
 
4.0%
Other values (14) 995319
22.2%
Uppercase Letter
ValueCountFrequency (%)
P 144178
21.0%
R 121216
17.7%
S 55417
 
8.1%
C 55314
 
8.1%
B 44485
 
6.5%
F 44315
 
6.5%
I 44239
 
6.5%
D 44115
 
6.4%
T 33084
 
4.8%
A 33024
 
4.8%
Other values (6) 66225
9.7%
Space Separator
ValueCountFrequency (%)
287955
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5175516
94.7%
Common 287955
 
5.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 629314
 
12.2%
t 497561
 
9.6%
n 453181
 
8.8%
i 386463
 
7.5%
a 376416
 
7.3%
o 277224
 
5.4%
s 254010
 
4.9%
r 233000
 
4.5%
m 209966
 
4.1%
c 177450
 
3.4%
Other values (30) 1680931
32.5%
Common
ValueCountFrequency (%)
287955
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5463471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 629314
 
11.5%
t 497561
 
9.1%
n 453181
 
8.3%
i 386463
 
7.1%
a 376416
 
6.9%
287955
 
5.3%
o 277224
 
5.1%
s 254010
 
4.6%
r 233000
 
4.3%
m 209966
 
3.8%
Other values (31) 1858381
34.0%

User's Credit Score
Real number (ℝ)

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean574.69472
Minimum300
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:42.361110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile327
Q1437
median574
Q3713
95-th percentile823
Maximum850
Range550
Interquartile range (IQR)276

Descriptive statistics

Standard deviation159.03092
Coefficient of variation (CV)0.27672243
Kurtosis-1.1998153
Mean574.69472
Median Absolute Deviation (MAD)138
Skewness0.002419055
Sum2.4137178 × 108
Variance25290.833
MonotonicityNot monotonic
2023-09-17T06:51:42.560115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
422 847
 
0.2%
720 842
 
0.2%
440 833
 
0.2%
408 830
 
0.2%
523 829
 
0.2%
719 827
 
0.2%
603 826
 
0.2%
525 824
 
0.2%
549 824
 
0.2%
430 823
 
0.2%
Other values (541) 411695
98.0%
ValueCountFrequency (%)
300 726
0.2%
301 767
0.2%
302 746
0.2%
303 797
0.2%
304 777
0.2%
305 784
0.2%
306 775
0.2%
307 813
0.2%
308 796
0.2%
309 792
0.2%
ValueCountFrequency (%)
850 741
0.2%
849 730
0.2%
848 764
0.2%
847 778
0.2%
846 780
0.2%
845 751
0.2%
844 692
0.2%
843 711
0.2%
842 784
0.2%
841 756
0.2%
Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
gmail.com
 
10753
yandex.co.uk
 
10717
yahoo.co.uk
 
10662
zoho.co.uk
 
10657
rediffmail.co.uk
 
10622
Other values (35)
366589 

Length

Max length16
Median length12
Mean length11.075131
Min length7

Characters and Unicode

Total characters4651555
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyahoo.co.uk
2nd rowprotonmail.co.uk
3rd rowcox.net
4th rowoutlook.com
5th rowaol.com

Common Values

ValueCountFrequency (%)
gmail.com 10753
 
2.6%
yandex.co.uk 10717
 
2.6%
yahoo.co.uk 10662
 
2.5%
zoho.co.uk 10657
 
2.5%
rediffmail.co.uk 10622
 
2.5%
gmail.co.in 10618
 
2.5%
aol.com 10614
 
2.5%
outlook.co.uk 10611
 
2.5%
rocketmail.co.uk 10608
 
2.5%
fastmail.co.uk 10579
 
2.5%
Other values (30) 313559
74.7%

Length

2023-09-17T06:51:42.749621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gmail.com 10753
 
2.6%
yandex.co.uk 10717
 
2.6%
yahoo.co.uk 10662
 
2.5%
zoho.co.uk 10657
 
2.5%
rediffmail.co.uk 10622
 
2.5%
gmail.co.in 10618
 
2.5%
aol.com 10614
 
2.5%
outlook.co.uk 10611
 
2.5%
rocketmail.co.uk 10608
 
2.5%
fastmail.co.uk 10579
 
2.5%
Other values (30) 313559
74.7%

Most occurring characters

ValueCountFrequency (%)
o 756093
16.3%
. 609429
13.1%
c 461890
9.9%
m 367176
 
7.9%
a 346602
 
7.5%
i 272462
 
5.9%
u 252421
 
5.4%
l 220177
 
4.7%
k 210650
 
4.5%
t 178314
 
3.8%
Other values (13) 976341
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4042126
86.9%
Other Punctuation 609429
 
13.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 756093
18.7%
c 461890
11.4%
m 367176
9.1%
a 346602
8.6%
i 272462
 
6.7%
u 252421
 
6.2%
l 220177
 
5.4%
k 210650
 
5.2%
t 178314
 
4.4%
n 167632
 
4.1%
Other values (12) 808709
20.0%
Other Punctuation
ValueCountFrequency (%)
. 609429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4042126
86.9%
Common 609429
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 756093
18.7%
c 461890
11.4%
m 367176
9.1%
a 346602
8.6%
i 272462
 
6.7%
u 252421
 
6.2%
l 220177
 
5.4%
k 210650
 
5.2%
t 178314
 
4.4%
n 167632
 
4.1%
Other values (12) 808709
20.0%
Common
ValueCountFrequency (%)
. 609429
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4651555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 756093
16.3%
. 609429
13.1%
c 461890
9.9%
m 367176
 
7.9%
a 346602
 
7.5%
i 272462
 
5.9%
u 252421
 
5.4%
l 220177
 
4.7%
k 210650
 
4.5%
t 178314
 
3.8%
Other values (13) 976341
21.0%

Merchant's Business Age
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.511448
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 MiB
2023-09-17T06:51:42.902287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum20
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.764577
Coefficient of variation (CV)0.54840943
Kurtosis-1.2068563
Mean10.511448
Median Absolute Deviation (MAD)5
Skewness-0.0025488305
Sum4414808
Variance33.230348
MonotonicityNot monotonic
2023-09-17T06:51:43.053151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
15 21291
 
5.1%
7 21194
 
5.0%
20 21163
 
5.0%
3 21141
 
5.0%
4 21137
 
5.0%
17 21113
 
5.0%
16 21095
 
5.0%
2 21081
 
5.0%
12 21079
 
5.0%
11 21064
 
5.0%
Other values (10) 208642
49.7%
ValueCountFrequency (%)
1 20644
4.9%
2 21081
5.0%
3 21141
5.0%
4 21137
5.0%
5 20941
5.0%
6 20621
4.9%
7 21194
5.0%
8 20964
5.0%
9 20735
4.9%
10 21016
5.0%
ValueCountFrequency (%)
20 21163
5.0%
19 20766
4.9%
18 21028
5.0%
17 21113
5.0%
16 21095
5.0%
15 21291
5.1%
14 20928
5.0%
13 20999
5.0%
12 21079
5.0%
11 21064
5.0%
Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
Time-Based OTP
 
11022
Authentication App
 
11004
Hardware Token
 
10904
Retina Scan
 
10893
CAPTCHA
 
10878
Other values (34)
365299 

Length

Max length37
Median length23
Mean length16.810771
Min length3

Characters and Unicode

Total characters7060524
Distinct characters48
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPattern Lock
2nd rowToken
3rd rowUSB Security Key
4th rowAuthentication App
5th rowSignature Verification

Common Values

ValueCountFrequency (%)
Time-Based OTP 11022
 
2.6%
Authentication App 11004
 
2.6%
Hardware Token 10904
 
2.6%
Retina Scan 10893
 
2.6%
CAPTCHA 10878
 
2.6%
SMS Code 10870
 
2.6%
Iris Scan 10858
 
2.6%
Fingerprint 10843
 
2.6%
Signature Verification 10842
 
2.6%
Mobile App Notification 10840
 
2.6%
Other values (29) 311046
74.1%

Length

2023-09-17T06:51:43.246916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
authentication 54017
 
6.3%
scan 43204
 
5.0%
verification 42927
 
5.0%
app 21844
 
2.5%
token 21682
 
2.5%
notification 21609
 
2.5%
mobile 21554
 
2.5%
confirmation 21494
 
2.5%
security 21492
 
2.5%
code 21450
 
2.5%
Other values (49) 569883
66.2%

Most occurring characters

ValueCountFrequency (%)
i 795853
 
11.3%
t 548977
 
7.8%
e 548929
 
7.8%
o 547962
 
7.8%
n 538457
 
7.6%
a 516863
 
7.3%
441156
 
6.2%
c 365504
 
5.2%
r 333373
 
4.7%
d 150806
 
2.1%
Other values (38) 2272644
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5412589
76.7%
Uppercase Letter 1131266
 
16.0%
Space Separator 441156
 
6.2%
Dash Punctuation 54059
 
0.8%
Open Punctuation 10727
 
0.2%
Close Punctuation 10727
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 795853
14.7%
t 548977
10.1%
e 548929
10.1%
o 547962
10.1%
n 538457
9.9%
a 516863
9.5%
c 365504
 
6.8%
r 333373
 
6.2%
d 150806
 
2.8%
u 140079
 
2.6%
Other values (13) 925786
17.1%
Uppercase Letter
ValueCountFrequency (%)
S 129545
11.5%
A 108390
9.6%
C 107577
9.5%
B 96547
 
8.5%
T 86822
 
7.7%
P 86228
 
7.6%
V 75086
 
6.6%
F 64597
 
5.7%
R 64364
 
5.7%
I 53806
 
4.8%
Other values (11) 258304
22.8%
Space Separator
ValueCountFrequency (%)
441156
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54059
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10727
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10727
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6543855
92.7%
Common 516669
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 795853
 
12.2%
t 548977
 
8.4%
e 548929
 
8.4%
o 547962
 
8.4%
n 538457
 
8.2%
a 516863
 
7.9%
c 365504
 
5.6%
r 333373
 
5.1%
d 150806
 
2.3%
u 140079
 
2.1%
Other values (34) 2057052
31.4%
Common
ValueCountFrequency (%)
441156
85.4%
- 54059
 
10.5%
( 10727
 
2.1%
) 10727
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7060524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 795853
 
11.3%
t 548977
 
7.8%
e 548929
 
7.8%
o 547962
 
7.8%
n 538457
 
7.6%
a 516863
 
7.3%
441156
 
6.2%
c 365504
 
5.2%
r 333373
 
4.7%
d 150806
 
2.1%
Other values (38) 2272644
32.2%

Fraudulent Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.2 MiB
0
210260 
1
209740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters420000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 210260
50.1%
1 209740
49.9%

Length

2023-09-17T06:51:43.426651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-17T06:51:43.600608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 210260
50.1%
1 209740
49.9%

Most occurring characters

ValueCountFrequency (%)
0 210260
50.1%
1 209740
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 420000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 210260
50.1%
1 209740
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common 420000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 210260
50.1%
1 209740
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 420000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 210260
50.1%
1 209740
49.9%

Interactions

2023-09-17T06:51:23.230595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:53.321309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:56.065801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:59.824612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:02.369652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:04.712384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:07.112778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:09.748573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:13.348395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:15.855243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:18.144353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:20.899893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:23.428017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:53.805440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:56.283745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:00.036730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:02.566308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:04.909237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:07.310895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:09.957115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:13.676208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:16.040315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:18.343409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:21.083772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:23.635038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:53.984633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:56.588959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:00.223442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:02.741755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:05.098983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:07.499739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:10.279767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:13.867213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:16.216387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:18.541375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:21.276159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:23.928868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:54.210332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:56.888817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:00.415031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:02.933862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:05.296097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:07.699556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:10.532667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:14.055923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:16.405359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:18.747346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:21.467906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:24.260415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:54.526109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:57.191185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:00.621605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:03.126110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:05.492300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:07.881228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:10.841419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:14.269192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:16.600888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:18.936880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:21.651685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:24.607394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:54.716156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:57.519658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:00.810351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:03.341203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:05.704395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:08.073868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:11.162494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:14.459894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:16.792021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:19.134360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:21.847018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:24.936936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:54.905043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:57.824651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:01.006649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:03.534493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:05.893882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:08.278198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:11.483495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:14.657871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:16.982912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:19.335279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:22.042072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:25.259475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:55.083935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:58.121064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:01.203496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:03.725122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:06.097241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:08.478365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:11.803012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:14.853594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:17.157450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:19.535238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:22.237622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:25.589421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:55.288662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:58.424920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:01.594192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:03.915809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:06.302463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:08.698930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:12.122119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:15.047999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:17.355560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:19.743345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:22.434603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:25.910087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:55.473082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:58.735796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:01.775943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:04.114332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:06.489635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:08.883557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:12.419242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:15.242250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:17.538910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:20.259224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:22.630207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:26.260350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:55.679370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:59.080211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:01.978670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:04.321069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:06.711846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:09.343596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:12.739571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:15.452413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:17.746593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:20.473370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:22.843369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:26.618474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:55.871105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:50:59.443477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:02.158289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:04.509510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:06.910108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:09.528790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:12.994203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:15.642978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:17.943232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:20.680403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-17T06:51:23.030814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-17T06:51:43.769577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Transaction IDUser IDTransaction AmountMerchant IDUser AgeUser IncomeLocation DistanceTime Taken for TransactionUser's Transaction HistoryMerchant's Reputation ScoreUser's Credit ScoreMerchant's Business AgePayment MethodCountry CodeTransaction TypeDevice TypeBrowser TypeOperating SystemMerchant CategoryUser OccupationUser GenderUser Account StatusTransaction StatusTransaction Time of DayUser's Device LocationTransaction CurrencyTransaction PurposeUser's Email DomainTransaction Authentication MethodFraudulent Flag
Transaction ID1.000-0.0000.000-0.0000.002-0.0010.001-0.0000.000-0.000-0.0010.0000.0010.0000.0000.0000.0000.0030.0000.0020.0000.0000.0010.0000.0000.0030.0000.0020.0000.000
User ID-0.0001.000-0.000-0.001-0.0020.0010.000-0.0020.001-0.002-0.0000.0000.0010.0000.0000.0000.0020.0030.0030.0020.0020.0000.0040.0000.0000.0030.0010.0010.0000.000
Transaction Amount0.000-0.0001.0000.003-0.002-0.0010.001-0.000-0.001-0.0020.0020.0000.0000.0040.0000.0040.0000.0000.0020.0010.0000.0000.0020.0000.0040.0030.0020.0000.0000.000
Merchant ID-0.000-0.0010.0031.000-0.001-0.002-0.002-0.004-0.002-0.002-0.000-0.0010.0020.0020.0000.0000.0010.0000.0030.0000.0020.0000.0000.0000.0000.0030.0000.0000.0000.002
User Age0.002-0.002-0.002-0.0011.0000.0000.0010.001-0.000-0.002-0.002-0.0010.0030.0000.0000.0000.0000.0000.0020.0020.0000.0000.0010.0000.0000.0000.0000.0020.0000.005
User Income-0.0010.001-0.001-0.0020.0001.000-0.0010.0010.000-0.002-0.000-0.0000.0020.0000.0010.0010.0000.0000.0020.0000.0020.0020.0000.0000.0000.0030.0000.0000.0000.003
Location Distance0.0010.0000.001-0.0020.001-0.0011.0000.000-0.000-0.0010.0020.0000.0000.0010.0040.0000.0040.0000.0000.0030.0000.0020.0000.0000.0030.0020.0020.0010.0000.000
Time Taken for Transaction-0.000-0.002-0.000-0.0040.0010.0010.0001.000-0.003-0.0000.005-0.0010.0030.0000.0010.0030.0000.0000.0010.0030.0000.0030.0000.0020.0020.0020.0030.0000.0030.002
User's Transaction History0.0000.001-0.001-0.002-0.0000.000-0.000-0.0031.000-0.002-0.000-0.0000.0040.0000.0020.0020.0000.0040.0000.0000.0000.0010.0000.0010.0000.0000.0000.0000.0010.001
Merchant's Reputation Score-0.000-0.002-0.002-0.002-0.002-0.002-0.001-0.000-0.0021.000-0.000-0.0020.0010.0030.0010.0020.0030.0000.0000.0020.0000.0020.0000.0000.0000.0000.0000.0030.0030.003
User's Credit Score-0.001-0.0000.002-0.000-0.002-0.0000.0020.005-0.000-0.0001.000-0.0010.0000.0000.0000.0000.0000.0030.0020.0000.0020.0000.0000.0000.0020.0000.0020.0020.0020.000
Merchant's Business Age0.0000.0000.000-0.001-0.001-0.0000.000-0.001-0.000-0.002-0.0011.0000.0020.0000.0000.0000.0000.0000.0010.0010.0000.0020.0000.0000.0010.0000.0040.0000.0010.000
Payment Method0.0010.0010.0000.0020.0030.0020.0000.0030.0040.0010.0000.0021.0000.0000.0000.0000.0020.0000.0010.0020.0000.0010.0020.0010.0010.0020.0000.0020.0020.000
Country Code0.0000.0000.0040.0020.0000.0000.0010.0000.0000.0030.0000.0000.0001.0000.0000.0000.0010.0000.0020.0000.0020.0000.0020.0000.0000.0020.0000.0010.0000.000
Transaction Type0.0000.0000.0000.0000.0000.0010.0040.0010.0020.0010.0000.0000.0000.0001.0000.0010.0010.0020.0010.0020.0020.0020.0000.0040.0020.0000.0000.0010.0000.000
Device Type0.0000.0000.0040.0000.0000.0010.0000.0030.0020.0020.0000.0000.0000.0000.0011.0000.0000.0020.0010.0020.0000.0000.0010.0040.0000.0020.0020.0000.0000.004
Browser Type0.0000.0020.0000.0010.0000.0000.0040.0000.0000.0030.0000.0000.0020.0010.0010.0001.0000.0000.0000.0000.0020.0000.0000.0040.0000.0010.0000.0000.0000.000
Operating System0.0030.0030.0000.0000.0000.0000.0000.0000.0040.0000.0030.0000.0000.0000.0020.0020.0001.0000.0010.0020.0020.0000.0000.0000.0010.0020.0020.0000.0000.004
Merchant Category0.0000.0030.0020.0030.0020.0020.0000.0010.0000.0000.0020.0010.0010.0020.0010.0010.0000.0011.0000.0010.0010.0000.0010.0040.0010.0000.0010.0000.0020.000
User Occupation0.0020.0020.0010.0000.0020.0000.0030.0030.0000.0020.0000.0010.0020.0000.0020.0020.0000.0020.0011.0000.0000.0040.0030.0000.0000.0000.0020.0010.0020.000
User Gender0.0000.0020.0000.0020.0000.0020.0000.0000.0000.0000.0020.0000.0000.0020.0020.0000.0020.0020.0010.0001.0000.0010.0020.0000.0000.0000.0000.0030.0000.000
User Account Status0.0000.0000.0000.0000.0000.0020.0020.0030.0010.0020.0000.0020.0010.0000.0020.0000.0000.0000.0000.0040.0011.0000.0000.0000.0020.0000.0020.0000.0010.000
Transaction Status0.0010.0040.0020.0000.0010.0000.0000.0000.0000.0000.0000.0000.0020.0020.0000.0010.0000.0000.0010.0030.0020.0001.0000.0000.0000.0010.0000.0000.0000.000
Transaction Time of Day0.0000.0000.0000.0000.0000.0000.0000.0020.0010.0000.0000.0000.0010.0000.0040.0040.0040.0000.0040.0000.0000.0000.0001.0000.0000.0000.0030.0010.0020.002
User's Device Location0.0000.0000.0040.0000.0000.0000.0030.0020.0000.0000.0020.0010.0010.0000.0020.0000.0000.0010.0010.0000.0000.0020.0000.0001.0000.0000.0010.0000.0010.005
Transaction Currency0.0030.0030.0030.0030.0000.0030.0020.0020.0000.0000.0000.0000.0020.0020.0000.0020.0010.0020.0000.0000.0000.0000.0010.0000.0001.0000.0000.0000.0020.000
Transaction Purpose0.0000.0010.0020.0000.0000.0000.0020.0030.0000.0000.0020.0040.0000.0000.0000.0020.0000.0020.0010.0020.0000.0020.0000.0030.0010.0001.0000.0000.0000.000
User's Email Domain0.0020.0010.0000.0000.0020.0000.0010.0000.0000.0030.0020.0000.0020.0010.0010.0000.0000.0000.0000.0010.0030.0000.0000.0010.0000.0000.0001.0000.0000.004
Transaction Authentication Method0.0000.0000.0000.0000.0000.0000.0000.0030.0010.0030.0020.0010.0020.0000.0000.0000.0000.0000.0020.0020.0000.0010.0000.0020.0010.0020.0000.0001.0000.005
Fraudulent Flag0.0000.0000.0000.0020.0050.0030.0000.0020.0010.0030.0000.0000.0000.0000.0000.0040.0000.0040.0000.0000.0000.0000.0000.0020.0050.0000.0000.0040.0051.000

Missing values

2023-09-17T06:51:28.139329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-17T06:51:30.171440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Transaction IDUser IDTransaction AmountTransaction Date and TimeMerchant IDPayment MethodCountry CodeTransaction TypeDevice TypeIP AddressBrowser TypeOperating SystemMerchant CategoryUser AgeUser OccupationUser IncomeUser GenderUser Account StatusTransaction StatusLocation DistanceTime Taken for TransactionTransaction Time of DayUser's Transaction HistoryMerchant's Reputation ScoreUser's Device LocationTransaction CurrencyTransaction PurposeUser's Credit ScoreUser's Email DomainMerchant's Business AgeTransaction Authentication MethodFraudulent Flag
088987240539537.762022-02-11 22:27:047314Direct DebitGRECashbackSmart Speaker157.37.179.160BrowshmacOSFitness & Nutrition28Police Officer58192.94FemaleStandardError3.7822.98Evening92.05SpainNOKRoyalty513yahoo.co.uk7Pattern Lock1
1422458575214787.212021-08-24 20:56:184508PayoneerFRAFineSmart Lock117.13.255.141FirefoxopenSUSEJewelry37Doctor94337.80Non-BinaryFreeDebit45.9236.17Nighttime652.03SpainRONRental Payment506protonmail.co.uk13Token1
2560754045128160.952021-07-05 05:54:222680CheckBRARentSmart TV153.107.127.180Otter BrowserWindows CEFurniture78Electrician72862.48FemaleMemberIn Progress69.2443.11Daytime592.04QatarIDNRent359cox.net6USB Security Key0
3361143297767110.532023-07-22 15:54:355719DiscoverRUSDonationVending Machine150.126.162.162FalkonDebianElectronics Repair44Chef95302.62FemaleMemberVoid5.267.44Daytime303.72United KingdomARSInsurance Premium533outlook.com9Authentication App1
4618883157780216.942023-05-23 08:19:008619PayoneerMEXAcquisitionVending Machine113.171.168.73Tor BrowserAIXWedding & Bridal48Engineer30098.36MaleInactiveUnder Review83.1843.91Evening603.60ChinaSARProduct Purchase450aol.com3Signature Verification1
5969121306401445.782021-08-15 02:51:234169ACH TransferBELPaymentGaming Console246.128.232.213BasiliskChrome OSHome & Garden75Pilot9107.93Non-BinarySuspendedPartially Declined55.0030.76Daytime241.80GreeceIDNPayout703yahoo.ca4Face ID1
6309263135069534.822022-07-29 18:12:156606CashIDNRechargeDrone20.55.183.167MaxthonHarmonyOSSubscription Services45Fashion Designer78788.59AgenderVIPClosed39.808.42Evening421.18SwitzerlandQARInsurance Premium680roadrunner.co.uk1Token0
7501177111135655.122022-06-15 18:08:543776Google WalletNORCharitySelf-Checkout Kiosk171.183.46.137NetSurfopenSUSEBooks & Literature31Plumber71685.33AgenderUnverifiedPending Payment60.1039.43Evening573.49MexicoSEKAcquisition669yahoo.ca15QR Code0
8662506821046446.392022-04-03 09:24:177805Contactless PaymentFRAInvoiceCash Register62.153.176.55NetSurfWindows ServerGifts & Souvenirs53Engineer8378.73Non-BinaryPremiumApproved82.866.02Nighttime584.01EgyptGBPConsultation Fee751cox.co.uk6Iris Scan1
998918994625281.422023-05-28 03:58:532857Money OrderIDNFineEmbedded System127.188.239.179NetSurfUnixFitness & Nutrition58Electrician42684.04GenderqueerExistingPending Payment46.3825.32Nighttime241.72Saudi ArabiaGBPConsultation Fee670tutanota.co.uk9Push Notification Confirmation0
Transaction IDUser IDTransaction AmountTransaction Date and TimeMerchant IDPayment MethodCountry CodeTransaction TypeDevice TypeIP AddressBrowser TypeOperating SystemMerchant CategoryUser AgeUser OccupationUser IncomeUser GenderUser Account StatusTransaction StatusLocation DistanceTime Taken for TransactionTransaction Time of DayUser's Transaction HistoryMerchant's Reputation ScoreUser's Device LocationTransaction CurrencyTransaction PurposeUser's Credit ScoreUser's Email DomainMerchant's Business AgeTransaction Authentication MethodFraudulent Flag
419990309417688645644.412023-02-04 19:22:073501ACH TransferIDNDepositSmart Mirror215.230.15.86ELinksFreeBSDBaby & Maternity37Teacher14038.43Prefer Not to SayStandardBlocked5.5651.44Daytime41.16Hong KongGBPUtility Payment799gmail.com10Password0
419991568241596650174.992023-03-22 10:40:464438Credit CardHKGReimbursementRobot45.187.152.135KonquerorRaspbianHome Improvement30Chef50821.61MaleProProcessing12.2527.94Daytime914.46United Arab EmiratesINRCharity Donation399yahoo.com19Smart Card0
419992250489601230116.592023-03-05 17:21:095865Apple PayNORRefundSmart Lock138.178.55.196SRWare IronSymbianEducation43Lawyer99350.35MaleLimitedExecuted73.4057.39Nighttime333.44PolandKRWRecharge536yahoo.co.uk19Time-Based OTP0
419993613488689138368.112021-06-22 11:54:475691Visa CheckoutUKPayoutSmart Thermostat81.231.241.31LinksWindowsClothing28Software Developer11793.21GenderqueerProPartially Approved18.5929.15Nighttime651.50GermanyUSDAcquisition791cox.net17Time-Based OTP0
419994112505428109317.572023-06-06 12:37:531746Cryptocurrency WalletBRAInvestmentHome Security System174.169.92.249LinksFirefox OSElectronics Repair18Researcher56120.86MaleInactivePending Payment27.4558.54Daytime203.83SpainVNDDonation to Nonprofit583tutanota.com20Fingerprint1
419995510036879103492.882022-12-04 10:02:064541American ExpressUAEGiftPOS Terminal214.212.80.15SilkGentooAppliances29Teacher8425.57FemaleClosedDeclined89.5519.41Daytime552.58ChinaPHPService Charge819roadrunner.co.uk3Voiceprint1
419996745752359699532.072021-07-28 17:18:384137Credit CardTHAScholarshipSmart Thermostat228.139.123.248SafariBlackBerryJewelry49Chemist53021.18OtherGuestAuthorized52.9754.35Nighttime601.99QatarINRBuyback838yandex.co.uk2Fingerprint0
419997719330572955935.022023-05-06 17:53:078414DiscoverISRRentalLaptop161.54.11.34DoobleAndroid WearBooks & Literature53Surveyor64137.82OtherClosedBlocked92.8449.27Nighttime681.75SingaporeILSConsultation Fee749outlook.com8Bluetooth Authentication1
419998406441904611152.322022-10-24 20:23:225639Prepaid CardIDNDividendIndustrial Controller55.145.35.3BraveRaspbianBaby & Maternity23Nurse38042.07Prefer Not to SayFreeAuthorized28.3629.00Evening863.39Hong KongTRYRent303rediffmail.com15Knowledge-Based Authentication0
419999486749681114293.742023-03-31 02:59:369166EthereumCHNService ChargeVending Machine15.208.138.102OperatvOSToys & Games73Salesperson61087.89OtherStandardCompleted17.7055.09Nighttime561.47MalaysiaNOKAcquisition535yahoo.co.in6Behavioral Biometrics0